{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/np/m42416h166j90krvxmfl40940000gp/T/ipykernel_49125/2278380608.py:3: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  solTchem = (data[1:,:]).astype(np.float)\n"
     ]
    }
   ],
   "source": [
    "data = np.genfromtxt(\"CMAQ_H2O2.dat\", dtype=str)\n",
    "Header = (data[0,:]).tolist()\n",
    "solTchem = (data[1:,:]).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "campsol = np.loadtxt(\"CMAQ_H2O2_results.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_indx = Header.index('A')\n",
    "B_indx = Header.index('B')\n",
    "C_indx = Header.index('C')\n",
    "t_indx = Header.index('t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Cells: 1\n",
      "Number of time iterations: 127.0\n"
     ]
    }
   ],
   "source": [
    "niterT, Nvars = np.shape(solTchem)\n",
    "Nsamples = len(np.where(solTchem[:,0]==-1)[0])\n",
    "print('Number of Cells:',Nsamples)\n",
    "print('Number of time iterations:',niterT/Nsamples)\n",
    "solTchem = solTchem.reshape( int(niterT/Nsamples), Nsamples,Nvars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff3998faa30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp=0 # cell number 0, camp only saved cell No 1 \n",
    "plt.figure()\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx], label=\"A\")\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx], label=\"B\")\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,C_indx], label=\"C\")\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff3485fe970>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,1],label='CAMP')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of A [mol/m3]')\n",
    "plt.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff350249fd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,3],label='CAMP')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of B [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff399978d30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,5],label='CAMP')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,C_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of B [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
